Performance of Wavelength Assignment Heuristicsin a Dynamic Optical Network with Adaptive

Routing and Traffic Grooming

Paulo Ribeiro L. Júnior‡, Michael Taynnan Barros† and Marcelo S. de Alencar‡

Institute for Advanceds Studies in Communications (Iecom)†Systems and Computing Department - DSC‡Eletrical Engeneering Department - DEE

Federal University of Campina Grande (UFCG), Campina Grande, Brazil

Email: {paulo,michael.taob,malencar}@iecom.org.br

Abstract—This paper present the performance analysis ofthe wavelength assignment four heuristics First-Fit, Random,Least-Used and Most-Used, considering adaptive routing andtraffic grooming capabilities in the network. The goal of thiscomparison is to verify if some of those algorithms present abetter performance with relation to First-Fit, considering thesecapabilities.

I. INTRODUCTION

Over the past several years, the volume of Internet traffic has

continued to grow rapidly. Bandwidth-intensive networking

applications, such as video-on-demand, IP telephony, and file

sharing using peer-to-peer network technology, consume a

large amount of network capacity, putting much pressure on

the network.

This growth of Internet services and users has meant that

arise the demand for quality of service (QoS) on the infras-

tructure of communications networks. This QoS is directly

linked to factors such as low delay in transmission, high

bandwidth available, high availability and low blocking prob-

ability. The Wavelength Division Multiplexing networks has

achieved increasing acceptance as mean of transport for the

promising traffic of the Internet and other sources that such

need characteristics of quality, due mainly to their physical

characteristics.

The users of these networks are linked by lightpaths, that

are routed and switched us by intermediaries nodes through

OADMs (Optical Add-Drop Multiplexers) and OXCs (Optical

Crossconnect). A lightpath is implemented by selecting a

path of physical links between the source and destination

edge nodes, and reserving a particular wavelength on each

of these links for the lightpath [1], in a process called routing

and wavelength assignment (RWA) problem [2], significantly

more difficult than the routing problem in electronic networks.

The additional complexity arises from the fact that routing

and wavelength assignment are subject to the following con-

straints: a lightpath must use the same wavelength on all the

links along its path from source to destination edge node and

all lightpaths using the same link (fiber) must be allocated

distinct wavelengths. This constrainsts is called Wavelength

Continuity Constraint.

Routing and wavelength assignment is an important problem

for the control plane of WDM networks and has received

extensive attention from the research community. Several RWA

algorithms have been developed for static routing, in which

case the demand of traffic does not change or changes with

large time intervals. This approach is interesting to the project

phase of network, when it is necessary to optimize the network

capacity. However, in practice, the traffic demand is dynamic,

i.e., changing randomly with time, and the applying these

optimization techniques to dynamic traffic is not practical due

their prohibitively large computation time.

Dynamic routing in the WDM network has been studied

extensively in the literature. In Mokhtar and Azizoglu [3],

an analytical model is developed for evaluating the blocking

performance of various routing algorithms, including adaptive

unconstrained routing which does not restricted the path

selection to any pre-defined set of routes. Brunato et. al. [4]

proposed load balancing algorithms through adaptive routing

for IP-based optical networks. Bhide et. al. [5] and Dante [6]

present new weight functions that exploit the correlation be-

tween blocking probability and the number of hops involved in

connection setup to increase the performance of the network.

Milliotis et. al. [8] address the same weight functions, but,

they extend the analysis to multifiber optical networks. Yoo et.al. [7] presents a new algorithm for adaptive routing based in

near-maximum number of available wavelength between two

nodes and evalue your blocking performance.

In Ribeiro [11], the use of two traffic engineering strategies

considered: load balancing, using adaptive routing, and traffic

grooming. The obtained results from this work show that

integration of adaptive routing algorithm, with traffic grooming

for routing and wavelength assignment, improves the system

performance with respect to blocking probability and load

distribution between the links of the network.

However, in almost all those papers the results are obtained

using only one wavelength assignment (WA) heuristics: the

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First-Fit algorithm.

This paper evaluates the performance of four WA heuristics:

First-Fit, Random, Least-Used and Most-Used, considering

adaptive routing and traffic grooming capabilities in the net-

work. The goal of this comparison is to verify if some of

those algorithms present a better performance with relation to

First-Fit, considering these capabilities.

The rest of the paper is organized as follows. Section II

presents a review of adaptive routing and traffic grooming.

Section III presents the WA heuristics studied in this work.

Section IV presents the simulation enviroment and the analysis

of the results. The Section V summarizes the paper.

II. ADAPTIVE ROUTING AND TRAFFIC GROOMING

A. Adaptive routing

In this paper, the routing algorithm selects a path using

adaptive routing, based on the state network information. In

this approach, each router periodically broadcasts its neigh-

boring link information to all other routers. This information

is used to construct its view of the network topology with the

associated link cost functions. Each router then independently

computes the shortest paths from itself to other destinations.

The network topology is represented as a graph G(V,E),in which V denotes the set of vertices (network nodes) and

E the set of edges (links). Each link (i, j) ∈ E is associated

with a weight wij which denotes the cost of using that link.

The weight function may be unity for all links (as in the RIP

protocol [6]) or it may incorporate link distance and dynamic

network information such as queue status, congestion, capacity

and reliability.

The cost is altered using the number of used wavelengths

as metric. To describe the cost function considered, let

P = {e1, e2, . . . , eL}, ∀ ei ∈ E, a path composed by Llinks, with i = 1, 2, 3, . . . , M , in which M is a maximum

number of active links of the network. The total cost of the

path P is computed as be sum of the its link costs,

CT,P =

L∑

i=1

Cej ,P , (1)

in which CT,P represent the total cost of the path P and Cei,P

is a individual cost of the link ei ∈ P .

The cost function used adds one to the cost value when a

connection is established and subtracts one from the cost value

when a connection is finished in the lightpath. Therefore, the

cost function is

Cnij =

{Cn−1

ij + 1, if a new connection is established,

Cn−1ij − 1, if an active connection is finished.

(2)

The initial condition of problem is the initial cost of all

links, C0ij = 1, ∀ (i, j) ∈ E. The setup of a connection

increases the cost value in eacu link of the connection and the

liberation of a connection decreases this cost. This situation

occurs up to maximum cost value Cij = ∞. This value

represents the occupation of all wavelengths on the link.

Therefore, if a connection was established in a route, the

cost of links of this route will be increased for the next

requisition, avoiding the occupation of these links. The result

of this operation is a uniform distribution of the load in the

network [11].

B. Traffic Grooming

The minimum granularity of a connection in a wavelength-

routed network is the capacity of a wavelength. The trans-

mission rate on a wavelength increases with advances in

the transmission technology. However, the requirement of

end-users such as Internet service providers, universities and

industries are still much lower than that of the wavelength

capacity. The bandwidth requirement is projected to increase

in the future; but, even doubling the current bandwidth would

be more than sufficient to handle the projected demand for

the near future. The current transmission rate on a wavelength

is 10 Gbit/s (OC-192). The 40 Gbit/s (OC-768) technology is

commercially available, however it is not widely deployed [1].

The large gap between the user requirement and the capacity

of a wavelength has forced the need for wavelength sharing

mechanisms that would allow more than one user to share

the wavelength channel capacity. Wavelength sharing, similar

to sharing a fiber using multiple wavelengths, can be done

in several ways. The approach used in this paper to share a

wavelength is to divide the wavelength bandwidth into sub-

channels.

III. WAVELENGTH ASSIGNMENT HEURISTICS

Here, four heuristics are considered in the comparison. They

are described in the following:

• First-Fit (FF): In first-fit, the wavelengths are indexed,

and a lightpath will attempt to select the wavelength

with the lowest index before attempting to select a

wavelength with a higher index. By selecting wavelengths

in this manner, existing connections will be packed into

a smaller number of total wavelengths, leaving a larger

number of wavelengths available for longer lightpaths;

• Random (RD):Another approach to choose between dif-

ferent wavelengths is to simply select one of the wave-

lengths at random. In general, first-fit will outperform

random wavelength assignment when full knowledge of

the network state is available. However, if the wavelength

selection is done in a distributed manner, with only

limited or outdated information, then random wavelength

assignment may outperform first-fit assignment. The rea-

son for this behaviour is that, in a first-fit approach, if

multiple connections are attempting to set up a lightpath

simultaneously, then it may be more likely that they will

choose the same wavelength, leading to one or more

connections being blocked.

• Least-Used (LU): The least-used approach attempts to

spread the load evenly across all wavelengths by selecting

the wavelength which is the least-used throughout the

network. This approaches require global knowledge.

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• Most-Used (MU): In most-used wavelength assignment,

the wavelength which is the most used in the rest of

the network is selected. This approach attempts to pro-

vide maximum wavelength reuse in the network. This

approach requires global knowledge.

IV. SIMULATION AND ANALYSIS

A. Simulation environment

A simulator was designed and developed to implement

routing and wavelength assignment in an all-optical networks,

using Python language.

In this simulator, when a new request arrives, the router uses

the routing table to determine the entire path from source to

destination. It then attempts to assign a wavelength along this

path by propagating a wavelength request to all the routers

along the path. If wavelength conversion is available in the

network, then a lightpath can be established using different

wavelengths on different links.

If this request fails, a different wavelength is chosen, the

choice can be based on the feedback from the closest node on

the shortest path. This process may be repeated till there is at

least one wavelength available. If this fails, then the request

is blocked, i.e. the lightpath can not be set up.

In the experiments we consider one regular mesh topology,

show in the Fig. 1, one irregular mesh topology, the NSF

network, show in the Fig. 2 and one ring topology, show in

the Fig. 3. Two scenarios are considered in the analysis. In

the first, we compare the four heuristics considering fixed and

adaptive routing. In the second, we compare the four heuristics

considering adaptive routing and traffic grooming.

Fig. 1. Regular mesh network with nine nodes.

In the simulation, we consider that each link has two

unidirectional fibers, containing 10 to 50 wavelengths, creating

a bidirectional link. Therefore, the cost attributed to each uni-

directional link may be different. The simulation stops when

the maximum number of requests is reached. The number of

requests is 20.000 for each load value. The load was set to

500 erlangs. Each connection was a duration or holding time

which is exponentially distributed and the arrival time which a

Poisson distribution. Each wavelength support 10 Gbits/s and a

granularity of 1 Gbits/s or multiple. A source-destination pair

of each request is randomly determined to consider uniformly

distributed traffic in the network.

Fig. 2. NSF network.

Fig. 3. Ring network with nine nodes.

The performance of the WA heuristics is compared in

terms of blocking probability, expressed as the fraction of the

rejected connection requests due to wavelength unavailability

divided by the total number of connection requests at the

simulation run.

B. Results

The graphics of blocking probability versus number of

wavelengths for regular mesh network are shown in the Fig. 4,

for the first scenario and in the Fig. 5, for the second scenario.

The Fig. 4 shows that up to 20 wavelengths, to the load

value considered, no difference between the use of fixed and

adaptive routing is shown. However, for more wavelengths, the

use of adaptive routing significantly reduces the incidence of

blocking in the network. Regarding the WA algorithms, there

is only a slight advantage of the First-Fit compared to other

algorithms.

This small difference between the performance of WA

algorithms is repeated for the graph shown in Fig. 5. This

graphic also shows that the use of traffic grooming as a tool

for traffic engineering brings a significant improvement with

respect to adaptive routing.

Regarding the first scenario, the behavior described for the

regular mesh is also observed for the NSF network, as shown

in Fig. 6 and the ring network, as shown in Fig. 8, differing

only the limits which the routing adaptive becomes more

efficient.

Also observed the same characteristic behavior of the al-

gorithms when observed in the second scenario, both for the

NSF network (Fig. 7) and for the ring network (Fig. 9).

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Fig. 4. Blocking probability versus number of wavelengths for the regularmesh network in the first scenario.

Fig. 5. Blocking probability versus number of wavelengths for the regularmesh network in the first scenario.

V. CONCLUSION

In this paper we analyze the performance of four algorithms

for allocating wavelength in WDM optical networks consid-

ering their dynamics performance with fixed, adaptive routing

and traffic aggregation.

The results indicate that the use of adaptive routing im-

proves the performance of RWA algorithm in order to decrease

the numbers of wavelengths needed to have a given value of

blocking probability, compared with the use of fixed routing.

However, this difference depends strongly on the topology

used.

In the comparison between the use of adaptive routing

Fig. 6. Blocking probability versus number of wavelengths for the NSFnetwork in the first scenario.

Fig. 7. Blocking probability versus number of wavelengths for the NSFnetwork in the first scenario.

and traffic grooming, we observed that the second technique

provides a lower number of blocked requests compared to the

first technique.

However, in the situation studied in this work, in the absence

of significant differences between the algorithms, we conclude

that the use of algorithms with lower computing cost, such as

First-Fit and Random, is a more attractive approach.

ACKNOWLEDGMENT

The authors would like to thank CAPES and CNPq for

funding this work and Iecom for providing the equipment and

facilities.

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Fig. 8. Blocking probability versus number of wavelengths for the ringnetwork in the first scenario.

Fig. 9. Blocking probability versus number of wavelengths for the ringnetwork in the first scenario.

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